Dynamic Storage Cluster Troubleshooting via Cross-Configuration Modeling
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Solution Overview
Problem
Administrators of remotely accessed storage cluster systems face challenges in diagnosing and maintaining these systems due to their unique configurations and varying complexities, which complicates the application of solutions across different systems.
Innovation Solution
A system comprising a collection server, an aggregation server, and an analysis server that collects and analyzes configuration data from multiple storage cluster systems to generate models for different configurations and usage types, predicting failures and selecting appropriate solutions for maintenance and upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If storage cluster systems use diverse hardware and software configurations to meet varying application requirements, then system adaptability and fault tolerance are improved, but system complexity and difficulty in diagnosing and maintaining systems increase
Solution Approach 1:
The patent segments the complex storage cluster system into multiple independent components (hardware modules, software components, configuration parameters) that can be individually analyzed and diagnosed. This segmentation allows administrators to manage complexity by breaking down the overall system into manageable parts while maintaining the ability to handle diverse configurations through modular analysis approaches.
Solution Approach 2:
The patent implements universal diagnostic and maintenance tools that can operate across multiple storage cluster system configurations. These tools are designed to be configuration-agnostic, automatically adapting to different hardware and software setups while providing consistent diagnostic capabilities, thereby maintaining system adaptability without proportionally increasing operational complexity.
2Adaptability or versatility
If each storage cluster system has unique configurations over time, then system versatility to meet changing needs is improved, but the ability to apply lessons learned across different systems deteriorates
Solution Approach 1:
The patent creates abstract models and representations of storage cluster systems that capture essential configuration characteristics and performance patterns. These models serve as transferable knowledge structures that can be applied across different physical systems, allowing lessons learned from one system to be copied and applied to others despite configuration variations. The models abstract away unique details while preserving transferable insights.
Solution Approach 2:
The patent implements feedback mechanisms that systematically collect diagnostic data, maintenance actions, and outcomes from various storage cluster systems. This feedback is aggregated and analyzed to generate transferable knowledge that improves future diagnostic and maintenance decisions across different systems, ensuring that lessons learned are captured and applied regardless of configuration differences.
3Ease of operation
If administrators manually diagnose and maintain multiple storage cluster systems with different configurations, then customization to specific system needs is improved, but time consumption and maintenance efficiency deteriorate
Solution Approach 1:
The patent implements dynamic diagnostic and maintenance systems that automatically adapt their behavior based on the specific configuration and state of each storage cluster system. Rather than requiring manual customization for each system, the system dynamically adjusts its diagnostic approach and maintenance recommendations to match the unique characteristics of each system being analyzed, thereby maintaining customization capability while reducing manual time investment.
Solution Approach 2:
The patent enables storage cluster systems to perform self-diagnosis and self-maintenance through automated monitoring, fault detection, and recommendation generation. The systems automatically analyze their own configuration and operational data to identify issues and suggest corrective actions, reducing the need for administrator intervention while maintaining the ability to handle configuration-specific requirements through automated adaptation.
Data Source
AI summary
Various embodiments are generally directed to techniques for dynamic diagnosis and/or prediction of trouble events in a storage cluster system and automated selection of solutions thereto. An apparatus includes a retrieval component to, in response to a trouble event with a first component of a storage cluster system at a usage level under a first usage type, retrieve a component model of a second component associated with a second usage type from a model database, wherein the second usage type comprises operations that differ from operations of the first usage type by no more than a predetermined threshold of difference; and a selection component to apply the first usage level to the component model to derive a resulting level of performance and determine whether to recommend installation of the second component in the first storage cluster system to address the trouble event based on the resulting level of performance.